Cellular telephones are used in a wide variety of different environments. People use cellular telephones while exercising, while driving an automobile, while watching television, and in countless other situations. These different environments, however, create a number of issues in terms of global positioning systems and sensor-based navigation systems for such devices. Navigation algorithms should be capable of performing properly regardless of the environment of the electronic device at any given moment. However, motion information is typically unknown to the navigation algorithm. The algorithm typically does not understand whether the device is being carried by an individual who is walking, riding in an automobile, or in any other type of activity.
Implementation of a motion sensor based navigation algorithm, particularly a pedestrian dead-reckoning (PDR) system, benefits greatly if the system is capable of identifying motion information so that a proper navigation algorithm can be selected. Traditional PDR systems are implemented assuming that that user is always walking or, in some cases, running. To ensure adequate navigation performance, and to be able to provide an improved user experience, autonomous motion tracking is therefore an essential feature.
Orientation algorithms utilizing accelerometers are based on measuring the direction of Earth's gravity vector. Due to basic principals of acceleration sensing, it is not possible to separate user-induced accelerations from gravity. This makes orientation measurement very unstable if the device is shaken or if the user is moving.
In many conventional systems, mean or median filtering is used to reduce motion-induced effects. However, in such systems, the orientation response becomes slow, and a certain amount of lag is introduced. This also occurs when there is no need to filter measurements, such as during periods of slow user movement.
During pedestrian navigation, step recognition algorithms search for footfall instances within a recognition window. The recognition window is predetermined to a generally applicable value. The performance of such algorithms varies depending upon the use case. Step detection algorithms are typically based upon peak- or zero-crossing-searches from accelerometer measurements. When an individual is walking, footfall frequency remains fairly constant. This makes it possible to predict when the next footfall will occur. However, during pedestrian navigation, different use situations and environments will affect a user's motions and movements. For example, a user may abruptly stop walking, the user might start running, or the electronic device may suddenly be moved to a different position on the user's body. These changes in motion and environment pose significant challenges to the step detection algorithm.
Another important issue for such navigation systems is power consumption. For a Global Navigation Satellite System (GNSS) receiver integrated into a cellular phone, power-consumption becomes a serious issue. Because only a limited reservoir of power is available in a cellular telephone, every module inside the telephone ideally should consume as little power as possible. Possible modules inside the telephone include a GPS system, a Bluetooth receiver, a wireless local area network (WLAN) module, a camera, a cellular transmitter (Tx) and receiver (Rx), and motion sensors. All of these modules compete for power inside the telephone or other electronic device.
The limited power reservoir of the electronic device sets bounds for sensor activity during pedestrian navigation. It would be advantageous to be able to maintain the same level of performance with a reduced power-on time. Traditionally, the sensors in such systems have remained on at all times while navigating. However, this can severely drain power from the system, requiring frequent recharging and hindering the usefulness of the device.
There are many ways to reduce the power consumption of an electrical or electronic device. These methods include miniaturization, selecting less power consuming components, and implementing partial or complete module shutdowns. Partial or complete shutdowns are possible if the system maintains the same level of performance with the reduced power-on time. For example, integrated motion sensors can be utilized to extract motion information to implement the reduced power-on time functionality without suffering degradation in performance.
Additionally, the power consumption of the sensor unit is still quit high in such a system, although the GNSS receiver may be powered down. Conventionally, the sensor unit has used a constant sampling rate and therefore is always powered regardless of whether the device is moving or not and regardless of the user's particular motions. Furthermore, different applications require different frequency range for proper operation. A normal sampling rate for gesture recognition is around 100 Hz or more. In pedestrian navigation, 20-40 Hz is an adequate frequency range. Using activity/motion detection, sampling can be lowered to a much lower rate of around 3-5 Hz, if the device remains stationary. When the device is moved, the data rate is again increased.